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MNASNet05

Image Classification
PyTorch
LiteRT
backbone
android
Model card Files Community

MNASNet05: Optimized for Mobile Deployment

Imagenet classifier and general purpose backbone

MNASNet05 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.

This model is an implementation of MNASNet05 found here.

This repository provides scripts to run MNASNet05 on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.image_classification
  • Model Stats:
    • Model checkpoint: Imagenet
    • Input resolution: 224x224
    • Number of parameters: 2.21M
    • Model size (float): 8.45 MB
    • Model size (w8a16): 2.79 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
MNASNet05 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 2.313 ms 0 - 20 MB NPU MNASNet05.tflite
MNASNet05 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 2.252 ms 1 - 21 MB NPU MNASNet05.dlc
MNASNet05 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 1.002 ms 0 - 36 MB NPU MNASNet05.tflite
MNASNet05 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 1.424 ms 1 - 37 MB NPU MNASNet05.dlc
MNASNet05 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.714 ms 0 - 52 MB NPU MNASNet05.tflite
MNASNet05 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.713 ms 0 - 50 MB NPU MNASNet05.dlc
MNASNet05 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.051 ms 0 - 21 MB NPU MNASNet05.tflite
MNASNet05 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 1.053 ms 1 - 20 MB NPU MNASNet05.dlc
MNASNet05 float SA7255P ADP Qualcomm® SA7255P TFLITE 2.313 ms 0 - 20 MB NPU MNASNet05.tflite
MNASNet05 float SA7255P ADP Qualcomm® SA7255P QNN_DLC 2.252 ms 1 - 21 MB NPU MNASNet05.dlc
MNASNet05 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.72 ms 0 - 50 MB NPU MNASNet05.tflite
MNASNet05 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.717 ms 1 - 50 MB NPU MNASNet05.dlc
MNASNet05 float SA8295P ADP Qualcomm® SA8295P TFLITE 1.379 ms 0 - 29 MB NPU MNASNet05.tflite
MNASNet05 float SA8295P ADP Qualcomm® SA8295P QNN_DLC 1.315 ms 0 - 28 MB NPU MNASNet05.dlc
MNASNet05 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.722 ms 0 - 49 MB NPU MNASNet05.tflite
MNASNet05 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.715 ms 0 - 49 MB NPU MNASNet05.dlc
MNASNet05 float SA8775P ADP Qualcomm® SA8775P TFLITE 1.051 ms 0 - 21 MB NPU MNASNet05.tflite
MNASNet05 float SA8775P ADP Qualcomm® SA8775P QNN_DLC 1.053 ms 1 - 20 MB NPU MNASNet05.dlc
MNASNet05 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.722 ms 0 - 50 MB NPU MNASNet05.tflite
MNASNet05 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.713 ms 0 - 49 MB NPU MNASNet05.dlc
MNASNet05 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.597 ms 0 - 51 MB NPU MNASNet05.onnx.zip
MNASNet05 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.475 ms 0 - 32 MB NPU MNASNet05.tflite
MNASNet05 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.475 ms 0 - 33 MB NPU MNASNet05.dlc
MNASNet05 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.423 ms 0 - 29 MB NPU MNASNet05.onnx.zip
MNASNet05 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.462 ms 0 - 31 MB NPU MNASNet05.tflite
MNASNet05 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.447 ms 0 - 26 MB NPU MNASNet05.dlc
MNASNet05 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.353 ms 1 - 26 MB NPU MNASNet05.onnx.zip
MNASNet05 float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.866 ms 53 - 53 MB NPU MNASNet05.dlc
MNASNet05 float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.641 ms 5 - 5 MB NPU MNASNet05.onnx.zip
MNASNet05 w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 1.648 ms 0 - 19 MB NPU MNASNet05.dlc
MNASNet05 w8a16 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 0.951 ms 0 - 32 MB NPU MNASNet05.dlc
MNASNet05 w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.79 ms 0 - 22 MB NPU MNASNet05.dlc
MNASNet05 w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 1.019 ms 0 - 18 MB NPU MNASNet05.dlc
MNASNet05 w8a16 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 2.634 ms 0 - 21 MB NPU MNASNet05.dlc
MNASNet05 w8a16 SA7255P ADP Qualcomm® SA7255P QNN_DLC 1.648 ms 0 - 19 MB NPU MNASNet05.dlc
MNASNet05 w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.792 ms 0 - 21 MB NPU MNASNet05.dlc
MNASNet05 w8a16 SA8295P ADP Qualcomm® SA8295P QNN_DLC 1.232 ms 0 - 24 MB NPU MNASNet05.dlc
MNASNet05 w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.793 ms 0 - 21 MB NPU MNASNet05.dlc
MNASNet05 w8a16 SA8775P ADP Qualcomm® SA8775P QNN_DLC 1.019 ms 0 - 18 MB NPU MNASNet05.dlc
MNASNet05 w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.794 ms 0 - 21 MB NPU MNASNet05.dlc
MNASNet05 w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 35.164 ms 4 - 118 MB NPU MNASNet05.onnx.zip
MNASNet05 w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.526 ms 0 - 32 MB NPU MNASNet05.dlc
MNASNet05 w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 29.825 ms 0 - 950 MB NPU MNASNet05.onnx.zip
MNASNet05 w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.434 ms 0 - 24 MB NPU MNASNet05.dlc
MNASNet05 w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 29.465 ms 9 - 686 MB NPU MNASNet05.onnx.zip
MNASNet05 w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.937 ms 13 - 13 MB NPU MNASNet05.dlc
MNASNet05 w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 36.25 ms 14 - 14 MB NPU MNASNet05.onnx.zip

Installation

Install the package via pip:

pip install qai-hub-models

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.mnasnet05.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.mnasnet05.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.mnasnet05.export

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.mnasnet05 import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.mnasnet05.demo --eval-mode on-device

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.mnasnet05.demo -- --eval-mode on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on MNASNet05's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of MNASNet05 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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